Representative color extracting method and apparatus based on human color sense and data histogram distributions

ABSTRACT

A histogram generating section generates a histogram for each channel of a color space which shows colors of pixels constituting an image, in which tone levels of said channel are divided into plural intervals as classes, and frequency of appearance of tone levels of the pixels in each interval is shown as degree. An interval extraction section extracts the intervals in each of which the frequency of appearance becomes local maximal value. A score calculation section calculates scores of the extracted intervals. The score indicates visibility of color to human based on human color sense. An interval selection section selects one or more extracted interval to be representative color, based on the calculated scores. A representative color extraction section generates the representative colors based on tone levels of the selected intervals.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method and an apparatus forextracting a representative color which represents visual impression ofan image.

2. Description of the Related Arts

Recently, digital cameras and camera phones are becoming popular, andusers are becoming to serve captured images in a personal computer athome, apply image processing such as color correction and image qualitycorrection to the images, and order prints through the Internet. Foraccommodating such user's needs, various techniques for easy search ofimages by sorting and organizing enormous number of images aredeveloped. Especially, a technique in which a representative colorrepresenting visual impression of an image is used as an index forsorting and organizing images has been known (see Japanese PatentLaid-Open Publications No. 10-289242 and No. 2003-216948).

The Japanese Patent Laid-Open Publication No. 10-289242 detects an areawhere hue changes most in an image (high-visibility area for human) andextracts a representative color based on the detected area (called asthe local representative color extraction processing). When therepresentative color is not extracted with the local representativecolor extraction processing, the representative color is extracted basedon a frequency of hue of a full screen (called as the globalrepresentative color extraction processing). When the representativecolor is not extracted with the local representative color extractionprocessing, the global representative color extraction processing is notperformed. Then a sensitivity vector that shows the correspondencerelation between the image and an impression word corresponding tocoloration is calculated based on the color difference between therepresentative color and the coloration that is preliminarily prepared,and the calculation result is stored.

To generate a histogram compatible with human color sense for extractinga representative color, the Japanese Patent Laid-Open Publication No.2003-216948 divides an HSV color space into a plurality of areas insaturation direction, reducing the division number in value directionfor an area with large saturation and increasing the division number inhue direction (since human is insensitive to color change caused byvalue change, and sensitive to hue change in this area), whileincreasing the division number in the value direction for an area withsmall saturation and reducing the division number in the hue direction(since human is sensitive to color change caused by value change, andinsensitive to hue change in this area). By using such histogram, areaswhere human is sensitive to color difference can be more preciselyanalyzed.

However, in the Japanese Patent Laid-Open Publication No. 10-289242,when whether the local representative color extraction processing or theglobal representative color extraction processing extracts therepresentative color, the other is not performed. Accordingly, it ispossible to extract an inappropriate color as the representative color.In addition, it will take a long time to constantly perform both of thelocal representative color extraction processing and the globalrepresentative color extraction processing, for solving this problem.

In the Japanese Patent Laid-Open Publication No. 2003-216948, colors towhich human is insensitive to color difference (similar colors) are notextracted as the representative colors. However, there may be a casethat similar colors are suitable as the representative colors.Accordingly, it is possible to extract inappropriate colors as therepresentative colors.

SUMMARY OF THE INVENTION

An object of the present invention is to provide a method and anapparatus for extracting a representative color which certainlycorresponds to human color sense.

In order to achieve the above and other objects, a representative colorextracting method of the present invention comprises a histogramgenerating step, an interval extraction step, a score calculation step,an interval selection step and a representative color generating step.In the histogram generating step, a histogram for each channel of acolor space which shows colors of pixels constituting the image isgenerated. In the histogram, tone levels of the channel are divided intoplural intervals as classes, and frequency of appearance of tone levelsof the pixels in each interval are shown as degree. In the intervalextraction step, the intervals in which the frequencies of appearancebecome local maximal value are extracted. In the score calculation step,a score of each of extracted intervals is calculated. The scoreindicates visibility of colors to human in consideration of human colorsense. In the interval selection step, the intervals to be therepresentative color are selected from the extracted intervals accordingto the score of each interval. In the representative color generatingstep, the representative color is generated based on the tone levels ofthe selected intervals.

It is preferable that a reduction processing step is provided forreducing a size of the image before the histogram generating step.

It is preferable that the score is calculated according to the frequencyof appearance in the score calculation step.

It is preferable that a histogram of HSV color space whose channels arehue, saturation and brightness is generated in the histogram generatingstep, and the score is calculated according to at least one of the hue,saturation and brightness in the score calculation step.

It is preferable that the score is calculated according to positions ofpixels which have tone levels within the extracted interval in the scorecalculation step.

It is preferable that the intervals whose scores are ranked in the top n(n is natural number), or the intervals whose scores are over apredetermined threshold value, are selected as the intervals to be therepresentative color in the interval selection step.

It is preferable that a center value, a minimum value or a maximum valueof each of the selected intervals or an average tone level of the pixelswithin each of the selected intervals is determined as the tone level ofthe representative color in the representative color generating step.

It is preferable that a representative color displaying step is providedfor displaying the generated representative color. In addition, it ispreferable that a representative color storing step is provided forstoring the generated representative color.

A representative color extracting apparatus of the present inventioncomprises a histogram generating section, an interval extractionsection, score calculation section, an interval selection section and arepresentative color generating section. In the histogram generatingsection, a histogram for each channel of a color space which showscolors of pixels constituting the image is generated. In the histogram,tone levels of the channel are divided into plural intervals as classes,and frequency of appearance of tone levels of the pixels in eachinterval are shown as degree. In the interval extraction section, theintervals in which the frequencies of appearance become local maximalvalue are extracted. In the score calculation section, a score of eachof extracted intervals is calculated. The score indicates visibility ofcolors to human in consideration of human color sense. In the intervalselection section, the intervals to be the representative color areselected from the extracted intervals according to the score of eachinterval. In the representative color generating section, therepresentative color is generated based on the tone levels of theselected intervals.

According to the representative color extracting method and therepresentative color extracting apparatus of the present invention,since the histogram showing distribution of colors of the image isgenerated, the score of each color is calculated in consideration ofhuman color sense, and the representative color is generated based onthe score, the representative color which certainly corresponds to humancolor sense can be extracted.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other subjects and advantages of the present inventionwill become apparent from the following detailed description of thepreferred embodiments when read in association with the accompanyingdrawings, which are given by way of illustration only and thus are notlimiting the present invention. In the drawings, like reference numeralsdesignate like or corresponding parts throughout the several views, andwherein:

FIG. 1 is a schematic view showing a hardware composition of an imageprocessing system;

FIG. 2 is a block diagram showing an internal composition of a personalcomputer;

FIG. 3 is a block diagram showing a composition of a representativecolor extraction section;

FIG. 4 is an explanatory diagram showing examples of histograms;

FIG. 5 is an explanatory diagram showing a representative colorextraction window; and

FIG. 6 is a flowchart showing processing procedures for extractingrepresentative color.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In FIG. 1, an image processing system 2 is provided to import image dataobtained with a digital camera 10, or image data recorded in a recordingmedium 11, such as a memory card or CD-R, into a personal computer(hereinafter, abbreviated as PC) 12 which performs various imageprocessing to the image data. The image data recorded in the recordingmedium 11 includes images on photo films digitized into TIFF or JPEGformat.

The digital camera 10 is connected to the PC 12 through, for instance, acommunication cable compliant with IEEE 1394 or USB (Universal SerialBus), a wireless LAN or the like, and communicate data with the PC 12mutually. The recording medium 11 exchanges data with the PC 12 througha specific driver.

The PC 12 includes a monitor 13 and an operating section 14 constitutedof a keyboard and a mouse. In FIG. 2, a CPU 20 is connected to allcomponents of the PC 12 through a data bus 21, and comprehensivelycontrols operations of the PC 12. As the components of the PC 12, thereare a RAM 22, a hard disk drive (hereinafter, abbreviated as HDD) 23, acommunication interface (I/F) 24 and a display control section 25 inaddition to the above-described operating section 14.

The HDD 23 stores programs of a viewer soft for performing various imageprocesses on the PC 12 and image data of plural images read from thedigital camera 10 or recording medium 11, in addition to variousprograms and data for operating the PC 12. The CPU 20 reads each programfrom the HDD 23 and expands the program in the RAM 22, and sequentiallyexecutes the program. In addition, the CPU 20 operates each section ofthe PC 12 according to operation signal input from the operating section14.

Through the I/F 24, the PC 12 performs data communication with anexternal device such as the digital camera 10 or a communication networksuch as the Internet. The display control section 25 performs displaycontrol on the monitor 13, for example displays various windows relatingto the viewer soft on the monitor 13.

For loading the image data, the viewer soft is started by operation ofthe operating section 14. In the viewer soft, there is a mode forextracting a representative color of the image (the representative colorextraction mode), in addition to the mode for loading the image data.The representative color is a color which represents visual impressionof the image, for example a color which occupies large area of theimage.

When the representative color extraction mode is selected, arepresentative color extraction section 30 shown in FIG. 3 is built onthe CPU 20. The representative color extraction section 30 is composedof a reduction processing section 31, a histogram generating section 32,an interval extraction section 33, a score calculation section 34, aninterval selection section 35, and a representative color generatingsection 36.

The reduction processing section 31 applies reduction process such aspixel thinning to data of an image inputted to extract representativecolors (hereinafter the image is called as the input image, and the datais called as the input image data). Concretely, the reduction processingsection 31 reduces the size of the input image from for example1600×1200 pixels to for example 640×480 pixels, without losing thecharacter of the image. Then the reduction processing section 31 outputsthe reduced size of input image data into the histogram generatingsection 32. Note that although an appropriate reduced size is variableaccording to degree of precision of an object in an input image,characters of images may not be lost when general landscape images andthe like are uniformly reduced in the above-described size.

The histogram generating section 32 generates for example a histogram 40shown in FIG. 4. In this histogram, tone levels of RGB (red, green,blue) channels which show colors of pixels of reduced input image fromthe reduction processing section 31 are shown in eight bit data rangingfrom 0 to 255. The tone levels are divided into eight equal intervals(tone levels 0 to 31, 32 to 63, 64 to 95, 96 to 127, 128 to 159, 160 to191, 192 to 223, and 224 to 255), and the eight intervals are arrangedalong the horizontal axis (class) of the histogram 40. Frequency ofappearance of tone levels of pixels in each interval is shown along thevertical axis (degree) of the histogram 40.

The generation of the histogram 40 is performed by identifying a sectionof a tone level of each pixel and updating a frequency of appearance inthe identified interval. The histogram generating section 32 generates ahistogram 40 for each channel of RGB, and outputs data of generatedhistograms 40 to the interval extraction section 33.

Although tone levels are divided into the equal intervals in the aboveembodiment, there may be finely divided intervals and coarsely dividedintervals. In addition, a dividing manner may be different according toeach channel of RGB. The frequency of appearance may be shown either bynumber of appeared tone levels or by proportion of tone levels to allpixels.

Instead of or in addition to the histogram 40 of each channel of RGB, ahistogram of HSV (hue, saturation, brightness value) color space may begenerated. In this case, by the following conversion equations, the RGBchannels can be easily converted to the HSV channels.

The following are determined:

Value V=max {R, G, B} (maximum value of tone level of each channel ofRGB); and

Saturation S=(V−v)/V (note that v=min {R, G, B} (minimum value of tonelevel of each channel of RGB)).

Hue H (radian) is calculated as follows:When R=V, H=π/3(b−g);When G=V, H=π/3(2+r−b); andWhen B=V, H=π/3(4+g−r), whereinr=(V−R)/(V−v)g=(V−G)/(V−v)b=(V−B)/(V−v).

(Note that when H<0, 2π is added to H. In addition, to convert Hue H(radian) to Hue H (°), 180/π is multiplied to Hue H (radian)).

The interval extraction section 33 analyzes data of the histogram 40from the histogram generating section 32, to extract intervals in whichthe frequency of appearance become local maximal value, for each channelof RGB. Concretely, the interval extraction section 33 compares adjacentintervals about the frequency of appearance, and extracts an intervalhaving the frequency of appearance higher than both adjacent intervals.In the example of FIG. 4, the interval extraction section 33 extractsthe intervals of 96 to 127 and 192 to 223 for R, the intervals of 64 to95 and 128 to 159 for G, and the intervals of 32 to 63 and 192 to 223for B. Although the interval extraction section 33 extracts pluralintervals for each color in this example, there is also a case that theinterval extraction section 33 extracts only one interval. The intervalextraction section 33 outputs data of the extracted intervals toward thescore calculation section 34.

The score calculation section 34 calculates a score of the extractedinterval, based on at least one of following methods (a) to (e). Thescore is determined in consideration of human visual feature forextracting a representative color close to human color sense, andindicates how much attention the color attracts from human in otherwords, visibility of colors to human. Accordingly, the color firstlyattracts human's attention when the human sees an image (the human mightrecognize the color as the representative color) will have the highestscore.

(a) The score calculation section 34 calculates the score according tothe frequency of appearance. In this case, for example the interval inwhich the frequency of appearance is over a threshold level is given ahigh score, from the standpoint that the interval having higherfrequency of appearance is more eye-catching. Instead of above, degreeof increasing and decreasing of the score may be changed according tothe degree of frequency of appearance.

(b) The score calculation section 34 calculates the score according tohue, when the histogram of HSV is generated. In this case, for examplethe interval in which distance from green in the HSV color space (greenis eye-catching color according to human color sense) is no more than athreshold level is given a high score. Instead of above, degree ofincreasing and decreasing of the score may be changed according to thedegree of distance from green in the HSV color space.

(c) The score calculation section 34 calculates the score according tosaturation. In this case, for example the interval in which thesaturation is under a threshold level is given a low score. Instead ofabove, degree of increasing and decreasing of the score may be changedaccording to the degree of saturation. The reason is that generallyhuman focuses on colors with high saturation, and colors with lowsaturation (for example gray, white and black) are highly likely not tobe recognized as the representative color. For example, in an image of“a portrait of a person in a gray background” which is often found inso-called stock photos, an observer firstly focuses on a flesh color ofthe person, colors of clothes and so on, and does not focus on the graybackground even when the gray area is larger than the person in theimage. However, when the background has a color such as green whosesaturation is high, an observer tends to focus on the background.

(d) The score calculation section 34 calculates the score according tovalue. In this case, for example the interval in which the value isunder a lower threshold level is given a low score, and the interval inwhich the value is over an upper threshold level is given a low score.The intension is that low scores are given to colors which are highlylikely not to be recognized as the representative color (a color withtoo low value is near black, and a color with too high value is nearwhite).

(e) The score calculation section 34 calculates the score according topositions of pixels which have tone levels within the extractedinterval. In this case, for example the interval including the tonelevel of the pixel positioned closer to the center of the image (thecenter of the image is highly likely to be focused by human) is given ahigher score. Concretely, input image data is analyzed again to extractpixels having tone levels within the extracted intervals. Then the scorecalculation section 34 determines image regions where the extractedpixels are dominant (image regions in each of which there are theextracted pixels as a mass having a certain area), calculates distancesbetween the center of each region and the center of the image, and giveshigher score to the interval including the image region having theshorter distance (closer to the center of the image).

The score calculation section 34 outputs data of scores calculatedaccording to at least one of the methods (a) to (e), to the intervalselection section 35.

The interval selection section 35 selects the intervals whose scores areranked in the top n (n is natural number), or the intervals whose scoresare over a predetermined threshold value, as intervals to be therepresentative colors. The interval selection section 35 outputs data ofthe selected intervals to the representative color generating section36.

The representative color generating section 36 determines one of thecenter value, the minimum value and the maximum value of the intervalsselected by the interval selection section 35 as the tone levels of therepresentative color. In the example of FIG. 4, when the selectedintervals are R; 96 to 127, G; 64 to 95, and B; 32 to 63, the tonelevels of the representative color are the center values (R; 111 (or112), G; 79 (or 80), and B; 47 (or 48)), the minimum values (R; 96, G;64, and B; 32), or the maximum values (R; 127, G; 95, and B; 63).

Instead of above, the representative color generating section 36 maydetermine the average tone level of the pixels within the intervalselected by the interval selection section 35 as the tone level of therepresentative color. For example, when the selected interval is R; 96to 127, in which there are 10 pixels of tone level 100, 5 pixels of tonelevel 110 and 20 pixels of tone level 115, the tone level of therepresentative color is calculated by following equation:(100×10+110×5+115×20)/(10+5+20)=110

The representative color generating section 36 generates therepresentative color by combining the tone levels of channels of RGB (orHSV) calculated according to one of the above-described methods. Thenthe representative color generating section 36 outputs data of thegenerated representative color to the CPU 20.

The CPU 20 associates the input image data and the data of therepresentative color, and stores them in the HDD 23. Instead of above,the CPU 20 may store the data of the representative color in a filedifferent to a file storing the input image data, both are in the HDD23.

In the representative color extraction mode, a representative colorextraction window 50 shown in FIG. 5 is displayed on the monitor 13. Inthe representative color extraction window 50, a region 51 where theinput image is displayed and a region 52 where a list of therepresentative colors is displayed are provided.

In the region 51, there are a file dialog 53 where a thumbnail of theinput image and a pass of the destination of the input image in the HDD23 are displayed, and a select button 54 for selecting the input image.When the mouse of the operating section 14 is operated such that apointer 55 is moved to the select button and then the mouse is clicked,the file dialog is enlarged and a list of icons showing files andfolders stored in the HDD 23 are displayed in a hierarchical manner.Then the mouse is operated such that the pointer 55 is moved to an iconof the file of the desired image, to select the input image.

Before selecting the input image, the region 52 itself is not displayedin the window, or nothing is displayed in the region 52. After theselection of the input image and the extraction of the representativecolor as described above, a list of samples 56 of the representativecolors is displayed in the region 52. Below each sample 56, a color code57, which represents the tone level of the representative color byhexadecimal numeral, is displayed.

The color code 57 is displayed in form of for example “#000000”, inorder of RGB. When an image of a reef-fringed island floating on a bluesea is the selected input image as shown in FIG. 5, the representativecolors extracted by the representative color extraction section 30 are“#00FF00” (green), “#0000FF” (blue) and “#00BFFF” (light blue),displayed in this order from left. Although the order for listing therepresentative colors is not limited, for example the colors aredisplayed in descending order of score calculated by the scorecalculation section 34. Instead of or in addition to the color code 57,numeric value itself of the tone level may be displayed. Note that atthe bottom of the region 52, a scroll bar 58 is provided for scrollingthe thumbnails to be displayed.

Next, processing procedures of the image processing system 2 having theabove-described configuration are described with reference to aflowchart of FIG. 6. At first, the viewer soft is activated and therepresentative color extraction mode is selected, and then therepresentative color extraction section 30 is created on the CPU 20. Inaddition, the representative color extraction window 50 is displayed onthe monitor 13. The user operates the select button 54 with use of theoperating section 14, to select the input image from the file dialog 53.Then data of the selected input image is outputted to the CPU 20.

The input image data is inputted in the reduction processing section 31.The reduction processing section 31 reduces the size of the input imagedata without losing the character of the image. The input image with thereduced size is outputted to the histogram generating section 32.

The histogram generating section 32 generates the histogram 40 in whichthe tone levels of RGB channels showing colors of pixels of reducedinput image from the reduction processing section 31 are divided intoeight equal intervals shown along the horizontal axis (class), and thefrequencies of appearance of tone levels in all pixels for respectiveintervals are shown along the vertical axis (degree) The data of thehistogram 40 generated by the histogram generating sections 32 isoutputted to the interval extraction section 33.

In the interval extraction section 33, the intervals in which thefrequency of appearance become the local maximal value are extracted foreach channel of RGB, for example by comparing adjacent intervals aboutthe frequency of appearance. The data of the extracted intervals isoutputted to the score calculation section 34.

In the score calculation section 34, the scores of the extractedintervals are calculated according to at least one of (a) frequency ofappearance, (b) hue, (c) saturation, (d) value, and (e) positions ofpixels which have tone levels within the extracted interval. The data ofthe scores calculated by the score calculation section 34 is outputtedto the interval selection section 35.

The interval selection section 35 selects the intervals whose scores areranked in the top n, or the intervals whose scores are over thepredetermined threshold value. Then the representative color generatingsection 36 determines the center value, the minimum value, the maximumvalue of each interval or the average tone level of the pixels withineach interval selected by the interval selection section 35 as the tonelevel, to generate the representative color.

Then the representative color generating section 36 outputs data of thegenerated representative color to the CPU 20. The CPU 20 associates theinput image data and the data of the representative color, and storesthem in the HDD 23, or the CPU 20 stores the data of the representativecolor in the file different to the file storing the input image data,both are in the HDD 23. At the same time, the list of the samples 56 andthe color codes 57 of the representative colors are displayed in theregion 52 of the representative color extraction window 50. The data ofthe representative color stored in the HDD 23 is for example read outand used for searching images by color, which enables easy search ofimage.

As described above, since the intervals are extracted as the candidatesof the representative color with reference to the histogram, the scoresare given to the extracted intervals in consideration of human colorsense, and the representative color is generated (selected from thecandidates) according to the scores, the representative color closer tohuman color sense can be generated. In addition, since the reductionprocessing section 31 reduces the size of the input image before thegeneration of the histogram 40, amount of the data handled by thehistogram generating section 32 is reduced and processing time can bealso reduced.

In the score calculation by the score calculation section 34, colorswhich are hardly recognized as the representative color (such as colorswith low saturation or value) have relatively low scores, but do not beexcluded from possibility to be candidates of the representative color.Accordingly, even in a case that a color which is hardly recognized asthe representative color is obviously the representative color in animage, the color which is visually suitable as the representative colorcan be extracted as the representative color.

Although the input image is selected with use of the file dialog 53 inthe above embodiment, the file of the input image may be moved into therepresentative color extraction window 50 by drug and drop. In addition,it may be that the user selects whether storing of the data of therepresentative color is performed or not, after the result of extractionof the representative color is displayed on the representative colorextraction window 50.

The methods for generating the histogram, for extracting the intervals,for calculation of the scores, for selection of the intervals, forgenerating the representative color, the configuration of therepresentative color extraction window 50 and so on shown in the aboveembodiment are example, and the present invention is not limited above.

In the above embodiment, the image processing is performed on the PC 12with use of the viewer soft. However, the image processing may beperformed on a home page through the Internet. In this case, a hardwarecorresponding to the representative color extraction section 30 isprovided in a server for administrating the home page.

In the above embodiment, the representative color extraction section 30is built on the CPU 20 when the viewer soft is activated and therepresentative color extraction mode is selected. However, therepresentative color extraction section 30 may be incorporated in the PC12, in form of hardware such as a discrete circuit or a FPGA. Or therepresentative color extraction section 30 may be connected to the PC12, as an independent device.

Although the present invention has been fully described by the way ofthe preferred embodiments thereof with reference to the accompanyingdrawings, various changes and modifications will be apparent to thosehaving skill in this field. Therefore, unless otherwise these changesand modifications depart from the scope of the present invention, theyshould be construed as included therein.

1. A representative color extracting method for extracting arepresentative color which represents visual impression of an image,said method comprising steps of: a histogram generating step forgenerating a histogram for each channel of a color space which showscolors of pixels constituting said image, tone levels of said channelbeing divided into plural intervals as classes in said histogram, andfrequency of appearance of tone levels of said pixels in each intervalbeing shown as degree in said histogram; an interval extraction step forextracting intervals in each of which said frequency of appearancebecomes local maximal value; a score calculation step for calculating ascore of each of extracted intervals, said score indicating visibilityof color to human in consideration of human color sense; an intervalselection step for selecting one or more said extracted intervals to besaid representative color according to said score of each interval; anda representative color generating step for generating saidrepresentative color based on said tone levels of said selectedintervals.
 2. A representative color extracting method claimed in claim1, further comprising a reduction processing step for reducing a size ofsaid image before said histogram generating step.
 3. A representativecolor extracting method claimed in claim 1, wherein said score iscalculated according to said frequency of appearance in said scorecalculation step.
 4. A representative color extracting method claimed inclaim 1, wherein a histogram of HSV color space whose channels are hue,saturation and brightness is generated in said histogram generatingstep, and wherein said score is calculated according to at least one ofsaid hue, saturation and brightness in said score calculation step.
 5. Arepresentative color extracting method claimed in claim 1, wherein saidscore is calculated according to positions of pixels each of which hasthe tone level within said extracted interval in said score calculationstep.
 6. A representative color extracting method claimed in claim 1,wherein the intervals whose scores are ranked in the top n (n is naturalnumber), or the intervals whose scores are over a predeterminedthreshold value, are selected as said intervals to be saidrepresentative color in said interval selection step.
 7. Arepresentative color extracting method claimed in claim 1, wherein acenter value, a minimum value, or a maximum value of each of saidselected intervals or an average tone level of the pixels within each ofsaid selected intervals is determined as said tone level of saidrepresentative color in said representative color generating step.
 8. Arepresentative color extracting method claimed in claim 1, furthercomprising a representative color displaying step for displaying saidgenerated representative color.
 9. A representative color extractingmethod claimed in claim 1, further comprising a representative colorstoring step for storing said generated representative color.
 10. Arepresentative color extracting apparatus for extracting arepresentative color which represents visual impression of an image,said apparatus comprising: a processor programmed to control: ahistogram generating section for generating a histogram for each channelof a color space which shows colors of pixels constituting said image,tone levels of said channel being divided into plural intervals asclasses in said histogram, and frequency of appearance of tone levels ofsaid pixels in each interval being shown as degree in said histogram; aninterval extraction section for extracting intervals in each of whichsaid frequency of appearance becomes local maximal value; a scorecalculation section for calculating a score of each of extractedintervals, said score indicating visibility of color to human inconsideration of human color sense; an interval selection section forselecting one or more said extracted intervals to be said representativecolor according to said score of each interval; and a representativecolor generating section for generating said representative color basedon said tone level of said selected intervals.